Comparing Computer Versus Human Data Collection Methods for Public Usability Evaluations of a Tactile-Audio Display
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We present a public usability study that provides preliminary results on the effectiveness of a universally designed system that conveys music and other sounds into tactile sensations. The system was displayed at a public science museum as part of a larger multimedia exhibit aimed at presenting a youthsâ perspective on global warming and the environment. We compare two approaches to gathering user feedback about the system in a study that we conducted to assess user responses to the inclusion of a tactile display within the larger audio-visual exhibit; in one version, a human researcher administered the study and in the other version a touch screen computer was used to obtain responses. Both approaches were used to explore the publicâs basic understanding of the tactile display within the context of the larger exhibit. The two methods yielded very similar responses from participants; however, our comparison of the two techniques revealed that there were subtle differences overall. In this paper, we compare the two study techniques for their value in providing access to public usability data for assessing universally designed interactive systems. We present both sets of results, with a cost benefit analysis of using each in the context of public usability tests for universal design.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it